LieDetectionEEG.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LieDetectionTest.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Nusserle/LieDetection/blob/master/LieDetectionEEG.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "82sWqTGDEK4r",
        "colab_type": "text"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHfzwqcc2kkh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import preprocessing\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB \n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "import scipy\n",
        "from scipy import signal\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import optimizers\n",
        "from keras import losses\n",
        "from keras import metrics\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (18, 6)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UdawqQs8HCaH",
        "colab_type": "code",
        "outputId": "2fad3da0-6773-4125-8b43-385cd26fd60d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AQPWPmCUEV9H",
        "colab_type": "text"
      },
      "source": [
        "## Data Exploration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfJ1YjR5HPxd",
        "colab_type": "code",
        "outputId": "2c16eead-66d0-4947-e837-b8ade79bf8b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        }
      },
      "source": [
        "%cd drive/My Drive/Shared/BagOfLies/\n",
        "df = pd.read_csv(\"Annotations.csv\")\n",
        "df_eeg = df[['eeg', 'truth']].dropna().reset_index()\n",
        "df_eeg.head()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Errno 2] No such file or directory: 'drive/My Drive/Shared/BagOfLies/'\n",
            "/content/drive/My Drive/Shared/BagOfLies\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>eeg</th>\n",
              "      <th>truth</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>./Finalised/User_0/run_0/EEG.csv</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>./Finalised/User_0/run_1/EEG.csv</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>./Finalised/User_0/run_2/EEG.csv</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>./Finalised/User_0/run_3/EEG.csv</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>./Finalised/User_0/run_4/EEG.csv</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index                               eeg  truth\n",
              "0      0  ./Finalised/User_0/run_0/EEG.csv      1\n",
              "1      1  ./Finalised/User_0/run_1/EEG.csv      0\n",
              "2      2  ./Finalised/User_0/run_2/EEG.csv      0\n",
              "3      3  ./Finalised/User_0/run_3/EEG.csv      0\n",
              "4      4  ./Finalised/User_0/run_4/EEG.csv      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WrnYbAh6H8rp",
        "colab_type": "code",
        "outputId": "d69cdd91-e7fa-4059-d428-e659ec88ccff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 844
        }
      },
      "source": [
        "lengths = []\n",
        "for i, eeg in df_eeg.iterrows():\n",
        "  lengths.append(pd.read_csv(eeg[1]).iloc[:,1:-1].shape[0])\n",
        "fig, axs = plt.subplots(tight_layout=True)\n",
        "axs.set_title('EEG Sample Histogram')\n",
        "axs.set_xlabel('Samples per recording')\n",
        "axs.set_ylabel('Recordings per bin')\n",
        "axs.hist(lengths, bins=100)\n"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 2.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  2.,  6.,  6.,  5.,  5.,\n",
              "         5.,  7.,  4., 12.,  6., 15.,  8., 12.,  7.,  3.,  5.,  5., 12.,\n",
              "         3.,  4.,  2.,  3.,  4.,  7.,  4.,  1.,  0.,  3.,  2.,  0.,  4.,\n",
              "         1.,  2.,  4.,  1.,  2.,  1.,  4.,  0.,  1.,  0.,  1.,  0.,  1.,\n",
              "         1.,  0.,  2.,  1.,  1.,  1.,  1.,  0.,  1.,  0.,  1.,  1.,  0.,\n",
              "         0.,  1.,  0.,  1.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  1.,  0.,\n",
              "         1.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]),\n",
              " array([  59.  ,  107.08,  155.16,  203.24,  251.32,  299.4 ,  347.48,\n",
              "         395.56,  443.64,  491.72,  539.8 ,  587.88,  635.96,  684.04,\n",
              "         732.12,  780.2 ,  828.28,  876.36,  924.44,  972.52, 1020.6 ,\n",
              "        1068.68, 1116.76, 1164.84, 1212.92, 1261.  , 1309.08, 1357.16,\n",
              "        1405.24, 1453.32, 1501.4 , 1549.48, 1597.56, 1645.64, 1693.72,\n",
              "        1741.8 , 1789.88, 1837.96, 1886.04, 1934.12, 1982.2 , 2030.28,\n",
              "        2078.36, 2126.44, 2174.52, 2222.6 , 2270.68, 2318.76, 2366.84,\n",
              "        2414.92, 2463.  , 2511.08, 2559.16, 2607.24, 2655.32, 2703.4 ,\n",
              "        2751.48, 2799.56, 2847.64, 2895.72, 2943.8 , 2991.88, 3039.96,\n",
              "        3088.04, 3136.12, 3184.2 , 3232.28, 3280.36, 3328.44, 3376.52,\n",
              "        3424.6 , 3472.68, 3520.76, 3568.84, 3616.92, 3665.  , 3713.08,\n",
              "        3761.16, 3809.24, 3857.32, 3905.4 , 3953.48, 4001.56, 4049.64,\n",
              "        4097.72, 4145.8 , 4193.88, 4241.96, 4290.04, 4338.12, 4386.2 ,\n",
              "        4434.28, 4482.36, 4530.44, 4578.52, 4626.6 , 4674.68, 4722.76,\n",
              "        4770.84, 4818.92, 4867.  ]),\n",
              " <a list of 100 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
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V7ZXk7gu3HyYh2dR73pnkMUn2qKpLk7w8yTFJ3l1Vz0pySZInL69sAAAAAGBb2GxAWFWv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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 1296x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5zEqTVmSBaN2",
        "colab_type": "code",
        "outputId": "6e2f8b90-8cd1-4dee-d006-d5febede584c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        }
      },
      "source": [
        "df_sample = pd.read_csv(df_eeg.iloc[2,:][1]).iloc[:,1:-1]\n",
        "df_sample.head()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>F3 Value</th>\n",
              "      <th>F3 Quality</th>\n",
              "      <th>FC5 Value</th>\n",
              "      <th>5C5 Quality</th>\n",
              "      <th>F7 Value</th>\n",
              "      <th>F7 Quality</th>\n",
              "      <th>T7 Value</th>\n",
              "      <th>T7 Quality</th>\n",
              "      <th>P7 Value</th>\n",
              "      <th>P7 Quality</th>\n",
              "      <th>O1 Value</th>\n",
              "      <th>O1 Quality</th>\n",
              "      <th>O2 Value</th>\n",
              "      <th>O2 Quality</th>\n",
              "      <th>P8 Value</th>\n",
              "      <th>P8 Quality</th>\n",
              "      <th>T8 Value</th>\n",
              "      <th>T8 Quality</th>\n",
              "      <th>F8 Value</th>\n",
              "      <th>F8 Quality</th>\n",
              "      <th>AF4 Value</th>\n",
              "      <th>AF4 Quality</th>\n",
              "      <th>FC6 Value</th>\n",
              "      <th>FC6 Quality</th>\n",
              "      <th>F4 Value</th>\n",
              "      <th>F4 Quality</th>\n",
              "      <th>X Value</th>\n",
              "      <th>Y Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-121</td>\n",
              "      <td>0</td>\n",
              "      <td>-127</td>\n",
              "      <td>0</td>\n",
              "      <td>-125</td>\n",
              "      <td>0</td>\n",
              "      <td>-64</td>\n",
              "      <td>0</td>\n",
              "      <td>-99</td>\n",
              "      <td>0</td>\n",
              "      <td>-117</td>\n",
              "      <td>0</td>\n",
              "      <td>-148</td>\n",
              "      <td>0</td>\n",
              "      <td>-120</td>\n",
              "      <td>0</td>\n",
              "      <td>-101</td>\n",
              "      <td>0</td>\n",
              "      <td>-100</td>\n",
              "      <td>4728</td>\n",
              "      <td>-112</td>\n",
              "      <td>0</td>\n",
              "      <td>-138</td>\n",
              "      <td>0</td>\n",
              "      <td>-83</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-132</td>\n",
              "      <td>0</td>\n",
              "      <td>-124</td>\n",
              "      <td>0</td>\n",
              "      <td>-116</td>\n",
              "      <td>0</td>\n",
              "      <td>-154</td>\n",
              "      <td>0</td>\n",
              "      <td>-118</td>\n",
              "      <td>0</td>\n",
              "      <td>-123</td>\n",
              "      <td>0</td>\n",
              "      <td>-124</td>\n",
              "      <td>0</td>\n",
              "      <td>-139</td>\n",
              "      <td>0</td>\n",
              "      <td>-101</td>\n",
              "      <td>0</td>\n",
              "      <td>-125</td>\n",
              "      <td>4728</td>\n",
              "      <td>-112</td>\n",
              "      <td>0</td>\n",
              "      <td>-145</td>\n",
              "      <td>0</td>\n",
              "      <td>-73</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-126</td>\n",
              "      <td>0</td>\n",
              "      <td>-122</td>\n",
              "      <td>0</td>\n",
              "      <td>-120</td>\n",
              "      <td>0</td>\n",
              "      <td>-149</td>\n",
              "      <td>0</td>\n",
              "      <td>-118</td>\n",
              "      <td>0</td>\n",
              "      <td>-119</td>\n",
              "      <td>0</td>\n",
              "      <td>-82</td>\n",
              "      <td>0</td>\n",
              "      <td>-131</td>\n",
              "      <td>0</td>\n",
              "      <td>-98</td>\n",
              "      <td>0</td>\n",
              "      <td>-110</td>\n",
              "      <td>4728</td>\n",
              "      <td>-105</td>\n",
              "      <td>0</td>\n",
              "      <td>-164</td>\n",
              "      <td>0</td>\n",
              "      <td>-79</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-125</td>\n",
              "      <td>2064</td>\n",
              "      <td>-123</td>\n",
              "      <td>0</td>\n",
              "      <td>-123</td>\n",
              "      <td>0</td>\n",
              "      <td>-150</td>\n",
              "      <td>0</td>\n",
              "      <td>-128</td>\n",
              "      <td>0</td>\n",
              "      <td>-122</td>\n",
              "      <td>0</td>\n",
              "      <td>-119</td>\n",
              "      <td>0</td>\n",
              "      <td>-118</td>\n",
              "      <td>0</td>\n",
              "      <td>-101</td>\n",
              "      <td>0</td>\n",
              "      <td>-113</td>\n",
              "      <td>4728</td>\n",
              "      <td>-104</td>\n",
              "      <td>0</td>\n",
              "      <td>-134</td>\n",
              "      <td>0</td>\n",
              "      <td>-81</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-127</td>\n",
              "      <td>2064</td>\n",
              "      <td>-126</td>\n",
              "      <td>24</td>\n",
              "      <td>-121</td>\n",
              "      <td>0</td>\n",
              "      <td>-151</td>\n",
              "      <td>0</td>\n",
              "      <td>-122</td>\n",
              "      <td>0</td>\n",
              "      <td>-121</td>\n",
              "      <td>0</td>\n",
              "      <td>-169</td>\n",
              "      <td>0</td>\n",
              "      <td>-125</td>\n",
              "      <td>0</td>\n",
              "      <td>-100</td>\n",
              "      <td>0</td>\n",
              "      <td>-118</td>\n",
              "      <td>4728</td>\n",
              "      <td>-117</td>\n",
              "      <td>0</td>\n",
              "      <td>-63</td>\n",
              "      <td>0</td>\n",
              "      <td>-84</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   F3 Value  F3 Quality  FC5 Value  ...  F4 Quality  X Value  Y Value\n",
              "0      -121           0       -127  ...           0       21       24\n",
              "1      -132           0       -124  ...           0       22       24\n",
              "2      -126           0       -122  ...           0       22       24\n",
              "3      -125        2064       -123  ...           0       22       24\n",
              "4      -127        2064       -126  ...           0       22       24\n",
              "\n",
              "[5 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aO8zVzp7Mrum",
        "colab_type": "code",
        "outputId": "fe50ecb1-c092-46dd-9ce3-250b40632255",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df_filtered = df_sample.loc[:, df_sample.columns.str.contains('Value')]\n",
        "df_filtered = df_filtered.iloc[:,:-2]\n",
        "df_filtered.head()"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>F3 Value</th>\n",
              "      <th>FC5 Value</th>\n",
              "      <th>F7 Value</th>\n",
              "      <th>T7 Value</th>\n",
              "      <th>P7 Value</th>\n",
              "      <th>O1 Value</th>\n",
              "      <th>O2 Value</th>\n",
              "      <th>P8 Value</th>\n",
              "      <th>T8 Value</th>\n",
              "      <th>F8 Value</th>\n",
              "      <th>AF4 Value</th>\n",
              "      <th>FC6 Value</th>\n",
              "      <th>F4 Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-121</td>\n",
              "      <td>-127</td>\n",
              "      <td>-125</td>\n",
              "      <td>-64</td>\n",
              "      <td>-99</td>\n",
              "      <td>-117</td>\n",
              "      <td>-148</td>\n",
              "      <td>-120</td>\n",
              "      <td>-101</td>\n",
              "      <td>-100</td>\n",
              "      <td>-112</td>\n",
              "      <td>-138</td>\n",
              "      <td>-83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-132</td>\n",
              "      <td>-124</td>\n",
              "      <td>-116</td>\n",
              "      <td>-154</td>\n",
              "      <td>-118</td>\n",
              "      <td>-123</td>\n",
              "      <td>-124</td>\n",
              "      <td>-139</td>\n",
              "      <td>-101</td>\n",
              "      <td>-125</td>\n",
              "      <td>-112</td>\n",
              "      <td>-145</td>\n",
              "      <td>-73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-126</td>\n",
              "      <td>-122</td>\n",
              "      <td>-120</td>\n",
              "      <td>-149</td>\n",
              "      <td>-118</td>\n",
              "      <td>-119</td>\n",
              "      <td>-82</td>\n",
              "      <td>-131</td>\n",
              "      <td>-98</td>\n",
              "      <td>-110</td>\n",
              "      <td>-105</td>\n",
              "      <td>-164</td>\n",
              "      <td>-79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-125</td>\n",
              "      <td>-123</td>\n",
              "      <td>-123</td>\n",
              "      <td>-150</td>\n",
              "      <td>-128</td>\n",
              "      <td>-122</td>\n",
              "      <td>-119</td>\n",
              "      <td>-118</td>\n",
              "      <td>-101</td>\n",
              "      <td>-113</td>\n",
              "      <td>-104</td>\n",
              "      <td>-134</td>\n",
              "      <td>-81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-127</td>\n",
              "      <td>-126</td>\n",
              "      <td>-121</td>\n",
              "      <td>-151</td>\n",
              "      <td>-122</td>\n",
              "      <td>-121</td>\n",
              "      <td>-169</td>\n",
              "      <td>-125</td>\n",
              "      <td>-100</td>\n",
              "      <td>-118</td>\n",
              "      <td>-117</td>\n",
              "      <td>-63</td>\n",
              "      <td>-84</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   F3 Value  FC5 Value  F7 Value  ...  AF4 Value  FC6 Value  F4 Value\n",
              "0      -121       -127      -125  ...       -112       -138       -83\n",
              "1      -132       -124      -116  ...       -112       -145       -73\n",
              "2      -126       -122      -120  ...       -105       -164       -79\n",
              "3      -125       -123      -123  ...       -104       -134       -81\n",
              "4      -127       -126      -121  ...       -117        -63       -84\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXkjPgERFEAO",
        "colab_type": "code",
        "outputId": "1eed6c85-188f-4d5f-a000-f4cb9ffec3e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# EEG Signal from channel F3 with noise\n",
        "f3_channel_eeg = df_filtered.iloc[:,1:2]\n",
        "plt.plot(f3_channel_eeg)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe3b7ae35f8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
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TTeEZaN3TIVA6VME3AG6sSdn3GdoZKZol8+qPPpG3OF6kdEDQoZ/YgOcA+x4A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\nbbAmZYNJE7fnER/zyyscaJXEdfaNNCck2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lH/s0CdvaaZkCSgS+mOW0wHjlUS\nt/Spe0xOIcmI0Rj6sk0tDw7z9Zfb8XbQ4bQjwHFkuYrdc3KsdbuIWpFr9o2d2CyGusSDrlWj1XVq\nOtC1MMor2HPMdTl4qV8SJNPBfX8004Fr7+mgYpvmLM8sNTzXMQ46jBi0cFBLSw5b08FnlB4CTWSu\naBqJGpldIEI18XCjApALAi0oXeYsdbpdow7SxVIgA7JSChH4wlCtB3RmJrAmhF5QVhkbU0hS7jeB\n6WB0r5C0WW7s8qCDEpKM/qqyk8B07my4Mvqc6fAEq0F77nRd7YPEClXbuIzMF3f2k4QkQ09nqkym\ng9njl9dh5kGDRfLTmQ5yks2r6cCdDe7gEiOBYKus24Y1/f90TWaSbceI7lss6GC0oOT7Q4xtwI3F\ngNFIucgVz1ILmIwhQC/qgW9mYWnh4mUQgOlI24hrOujtJWo6iN40HVT3ioTyCl9A1QIHEXshDFjQ\ngZgOkRYKzQvxoEOoMg2cLmwE/Kyx4guhyiuSWmbaTBMVNOw36MBO3w64cRSt51MJkTba6jwWZ8o4\ntlrHWl3S9CWVvKO0AABTQ8JkNAkVpLQDzvIes+BIn+cqt6WvY1pwAmDtFav5jNLAMR/Z/5eOjXZ0\n2pGwJG99yFux8SnH84Rh9CfNCbQ/yq4SFZ9rOvCWmYTT1aYS5jOp6jpQQ5osnKpOa1hWkDkN/Nkh\nLYtqQkCWO+ouTRbALFtzlVes1ltYXm+qdZycqzwldDsTgw5yWxMFTf3npXuAnEvJoQt9gVLBR7er\nGW922RygHVuyRZqtjrH283JHXvdPaxw5L9Tpat3BAuFBB2I6ELSQpG90YZD7axrtOUk3gwcdAWkv\ntTo66EC1+Jxazp0W2p7J4mRBh1C3+6R7TvYfMWXaHRnAVUEHZ3mFZsNWowAqJQlcZZo2jkf1+hdv\nlcyN5bVmLOhAzBQAicwCFxrMuau32sqB5ywjgmY66LFA92DLZAHdrruzRbkgr6Pt6NNvbSHJpJJW\n36P23PL51cLDcU0HzuBQ5RWBtlXzsEDoPuq21vK4KDmomQ7xhCK1Gw0DL+rwkI/p0Gh1VdCh0eo6\ntXcA0wanbnpp7TmPLFexe8uEmnt4u1wZpI6CDcyOkYHfrhHcazg0HfRrs7xi2dIe4r5YVveKZrur\nkh8culMXK6+INP5a0TPgeXqtG5dXjDEU0MB7+lS8vKIQGTMq6OAw+ughcEXTVmpSxIw/QJVSoKLH\nU8qokMbA0ZV6rLyCxNoaBtMhPiw4fTz0vViG0y6vSKMwJ6Gsyiuo3lJfjySmAxlJ9LuQGeuAu2RB\n9UKfdQQd2G+1Wn/ceeSTE4l1AZo+xYU1lVGVpemQUF4RsgCH5wlWXsE1HdxMh4UpM7KcBK2V4Bv7\ntg3nImc65DBIuSNDDgKPVqv9WMZxkpOyWm85FwFbONKl6cANWE6Z9hxjljv5PCHSZfX4QmihKq3p\noLNL3CEig9Cum0/T+fB4hlWYQZpUTQf+G+bW6u4VdK10oCKxvMLzFJXc9yTLpuA7WmYqpoPO5PDW\nipKBxcoriFKdUAbgR0Gfble3c3QKSXbN66/KwPpc2ZI0HWzYWiG8nR+9JiP9kWNSPGq6FCrRQjv4\nJLdjBj9VKYGD6WCUFQxgqNAuKSiSBjofm42XBH4/XZoOAFG4PePekjiZChIYrdj4M2wa/fZlCKx5\nu82FJNk+WxHTQdYO6+2TIUqBBbkPLt4r2SGhL9Bs6Rpz0nQYpLzCmIOjuYiXSNjfpZbR9pysmA5s\nXi1Fjr9tHwCMDUhCkjlKaaiswnZKdQDDj5U2EnwvznQAtN1kMB2i8+aszkKU4Wx3upHWiZyvKKmi\n74nMKBeMcWe2luTlFVz8koQkCXTdTCamDqgWfHPeI7sG0E7wROijzYQkqRMIDzpQQHe90TYCM4Ry\n6CvHu2SUV5j3lzLPKrHANB10zX/keHom0yHwPSXOaAtSu0B2JHU9W6m3VGKK0OpoTQfVFjJPeUXL\nZjpEnZDqLeP3EwXfaOVIoGs4Fx2Pza4gochywXd+BsTLg0zxbn3fPaH3zZkOq4ypSWOX2/a1Zjtq\ni+tF+/NyBh2oVaip6XDaCiq5mA6qjacvjLbWWSDNBGJ68bXYKK9wMB1oznGWVyzXpNYaBT4DXb5Y\na8aZDp0uzcFWeUXLzXQAYJT8lAu+8md4y8xqQgKMw24Dz0HPlovp0OzIQEXgkWD0mOkwxpBAD9UR\nVl7Bs7J2eYUNWsA404FaXJ6OhGP4w8SDELpmUxoDR1frsfIKISRlPFPTITK0pkuhEgEDdIazH00H\nG6XQl1H9aLLgTrprsuT71eJdOcorVNAhubwC0FFZ18KimQ6ewWChSYW3EK0ldAZIOhcASiwS0FFr\nmkDpe3Jyl9+xNR1ovMznZDqooENgZqhtui7VqxIduRfwemxXa0aAt9O0gh2qvKLpDIrpIIPOSALx\n0hSCYM6DK0CRWF7RNfUT6DP6LheSdIkp2QKSWWsMD27x79q/4xoBQuhtm78xz5OzQpRQmV0THmXI\nSDSvFFEZY+UVFtOhECnRE6ZLoSkkqcYb1y5xMzsoo2wKScq/NtOBB2n6Ab9eaVmH0NcsEq7pwM+D\nMsDUrkzp8zTb+n6xy21nypI1HWyxxzxn5gZn82RdMzIUT64NznTQ3T5MTQf6TLa3jcormJAkvyd2\nsCJV0yFI1nTodLvKYedzi8r+su/6nh4jpPdATIfTPOjQjs9xvYA/O17E8Kg19Rxtf5c6PthjVmk6\nOMoriBHJnUn6jNavPEwHVV4x42Y6cPacS9Oh5jg2aj1plB+p8gqtxE/XnjQ56Dkk5547poDO9NZa\nJsMGkHMRMTwqpUA9oyQkSaB5jJ8LsQpW6y2jJKUSaTqs1FpGa/KyxXSYLoWYj7qIELizQplqfj94\nUNcsr4gC4JHNRE6gXbbaaOtA2fI6YzqwwEzoCRyPgg55WZMAcCFr/cxbplJZidJ0iI5xvdFSdfgA\nYjpjgGkDr9Zb6nmgDgeK4VEKVIDKDDrI1xQgt535WqRpwtkFrUirgBzQVE0HI+jA7Xo9r6wyTQe7\n5EIeU8doi8v1QADd3pf/v9nuqEBwJVZeYZbPcK0xgir58Ijp4J7f6f40o+4hrY7UTKCSJoORZpRX\nME2HaTNAaQc46q02njldw9JMSZd4Bb6h+aM1HbRvZGs6NEjTgTFdbUY4gWuzFJhNVE2wRTl0UjhZ\n6J/OsdvtqqAiCUd6Qvorzfb5o+kwbpk5YtCAO77WYG1jinjmdA0kJKkU9R0DzosWxnqrg3f8j6/B\n92QbpY9+9WkAwD1XbDeCDhQUmCoGmC2b7R+3VUpqMoq1zGQPjVPTITq2qVKgorWAdsxJoGaQ8oqp\nYoBPP3wMf//d45guBYaTkVReQQbqSr2lVMkNpgMTsaFFmqLru7ZMxI6VG4rX7J7FI8fWDCHByxYr\n+MdHT6qAiB1IUJoOZAB4njNL4YLqje6b52BnprWmgzbK7BpiMsCvWJxO3SeBAlNSGCyF6RBQ94pO\nahcAF+iYas12jLlil1fY40cHHRKYDtG1CdiiAcRbZqrvC17iEC1idrcIcsjs7hVsv7plpjZ45O89\nc+FlgSJ+fFmLTCHwsBY5ZjxIYlPhXd0rhIC7e0VgPqOeEFp3xGag+AK1qMWZ7wlMRUKjNBZXai2V\nIQFMTQd+nyaLPlodTUF2BSf5uCsEnnbu2lHLTEd5xe65CXz7uVUVXCyFXqymtDdwZz55G0LIrHgt\narPlCm7RvPud56ygQ6vDAkN6H/z8ZR2r6TASYqKiQyivaHU6mQEwymjbgoFJsJkGHCqb2mgj8M2A\nGrV2bqryCtaKzZrjXKKYav/su1zTgZxzJfjW0W00jfKKmhaVVZohLIvXaGkxwmaHlVc02n0FZTns\n7hWB5zlLkvh31+ot7LDuDY1hXrZGnQ1oPFIHCgCYsIShszQd5O/lOrprzgw6TBWl406UdSBOT/cE\nbymtmQYrkUg2h9Z00DoKlABptnXLTCAaz23dTYbOf7IY4OR6E7VmJ9IvsJgOoXZ0yqFsX14IPCN5\nQdfN1qyh/RR8D9uni3j2dB3FwMdkMcBTy1W85jc+h3uu2A5APuttJiRZDD0sRt1ufuLDX8K26RIO\n7ppR26egg63pQJDlFWaJK93LGNPBUV5BTmnoC3S7OnAT+EKxJfMwHQizrDyFl1fMTRZU/bondAnt\nGz7wj+o7b713P/7q68/ga0+dws+97Ar83EcexNtfdJnh2NH1ADTzamuliKdP1VAphTpZx/USGsR0\nKKj/f+QrR/Bjv/8lfOVd96DaiMorGpKZ8kdfeAJv/eOvYt+2KfzS910lr501fl2aDgAF8nVCguaV\ntQYTkrR0qkqhh1qjDU/AaMtK8/+JtQZue+//wnqjjT/5Nzdj+3QJL/zVvzPWBxqbtpDk8rrO5Bd8\nD9VOW+6v2cFavaXm1kopUPPeW//oK/jcI8fxf952N37pL7+J9/3td9V+vu+6nUaJUqPFmA6eGQif\nYON0W6UI3xPYHc0Zz52u485f/hu862VX4ro9W3Dde/4aALBzS9mwhxXTgbFk90bsINKZakZdaQA5\ndm02Lk9c2uUVqoOHstOAlQRmGQcPfNig+0rP2PJ60+qg04XvyTFEwa2kjmfnEsZBhxFjpd7CREEu\nXs+t1OEJ6dg9c7qGQhAtBtHE6HkCf/NTd+LYah1bJkKcqupWTo1WB5/77nH4nhQ+OrhrBvdcsR3P\nv3y7ou8C+mH67//yelwURZunigE+8IYbcGDnDJ48KUsBeMtMGYHXx5yu6RAYdLDXHt6Da/fMxlgB\neYUMP/nv7lAP59tetB+f/vVjzmOYTiivAGQQ58Ejsof31krREAEyqV3SILticRr/+TXX4AWXSwPA\npkoTfvEVV+Huy7bhiiXtuP/m/Yfwj4+exMyEzmgDMmK/Um8ppoNifvg6YprZMrOgKXUcZEjRNaEs\nlMcc50bbLFm4/ZIF/D/ffw3uO7CYuk/Ce15xFe69cgcu2zGtSkSAeKCEmA6NVr7yCgD48zffGkWr\nKbMY367d2i2u6ZDOdCBGBm03sP4PWN0RGJNBaTpY9HZdXmEyHbgIoFZsNoMOXBwP0NF+VV5hlQMk\nQV6HpsF0cDFmaDNc58MTJtuAZ+b5X0lxbhvvEXxPG6uBJ/Du770SxcBXxtPpWlO2a2MZStoOr+kO\nfM8QW3O15Lt8cRo//z1XotXp4oa9c/jqk6cAQAU8TPFE+ffXXnMNPv/d49g9J42Y19ywBwd2zmSW\nHHzqJ59n9M9W22U/y6qvXJgq4smTVWVAEiuEnldyzr/0xDIA6dx97alTqDXbcG359ksW8M6XXI5i\n6OPirVPq/F1BB0MUNeE4P/WTz1PGpo2P/thtUT28dux5gOr3f+gmwxkFpJP3W284hIO7ZpMuiQEa\nS0I45hFOc4/0Xaj1JCmiu4QkzVahZmAo1jKTfUYtM7vdLo6u1rE1Mn5p+81IE8A1t/DvegYDp6MC\nH7y8QrWvHBLTIfDMTjBxTQf5/6OrdcNRBfT1qbKuQIHv4f2vP4QDS/K726dL+JVXXY3jq3W89KrF\naJv5mQ4vPbiI0BfYv71ivP/K63dh7/yk7IiUUF7hRc8MENW5R/P+aq3lnIs8oZkOMqOtA3OcXdZs\nd3GqKr+3ZSJU2fDZiRBPnqyi1mgrh4lQDDzN8IgCEOsNSXW/c/82vPMll6Nc8HFR5OwsMFE8HkQv\nBB7+54/eiu88J+2yH7r9Inz0K0fwxcdO4nmXblXXQQpJarG6pZkyHju+jgePnMaOmZLaD6BLSvjx\nGszIUOtR0Pwzydh1fI53CUlqVo8HL9TPTeB7+E+vPIhXPnICe+YnkIX//VN34rmVuqH7wlumbpkM\ncWS5pkqc+Lg+fOEcHj++jr//znF87Sk5933qW88BAP7Tx7+FA0szEQNKP2s+Gz866BCo60TzG6Dn\nUSqdqbc6+M+ffAgAVPeW2YlQlSt9OwoWP/zcqqHVxZGk6eAJrtXGmA6sbIjbNIAMGJDGzfwk2YLa\nIf72syvKPvrqk6dwyTbZIvK1h3dj52wZk8UA1+6RczPdeyqNAeQ8PDdZQDEq2Zgph6g15b2i89pW\nKeLEWgP1Vht/9MUn1W8femYFizMlvO7GPfizLx/Bg0dOQwiBnbMF1CKNL85I43OxLQj//vsP4cDO\nGXzw84/jC4+dwKPH1/GFx05gZkKWH962bwH3XbWID3zmUQAyIKfYN422ekbf9qL9OLhrBrfum8d/\n+ZSHZkuXqjQVGzcp6KCf10n2Ps13geepOTMtgUrzVVL5OyADn+1OF+tsHW+2O1E7VcnobjLR0XMd\n46DDCEHiRYvzE3js+DqW1xtGfaMUctMUMF8IXLgwqer7CFLgro2nlquq/vpV1+/Cm+++BIA5sVAW\n/7ZLFoxt3HXZNgDAs5GQCQWASdWY1J/bna4SaOHgThU31hdnSyriCPReXrFvmzZUDu6axZtuuxDv\n//QjMYMtLau+NFvClx5fVq+fiSiKnjCDH4sRpUsIgZdfu1O9L4RQzjTfb7ng42VXLxn7mp0o4IVR\ntoKf75bJAhrtjl6cmLDmer03IUk7mp5aXhFtstnuGBlPIQS+95qdyIupYoD7yOA06oltxoGPekuW\nsuQxSAEoJ+U0o+3ZWWTOggDSu1e4xoKt6WDrF9jb5Nli15jlzgVneXZkz8xoG5rp4KvnQ4t38ftN\njoHNrsi6hFxNmX7jcma4MKXuXiGMa2WzP7joYpqmA90T3xfqXn49MgpPV1sGfVppOvhxwTqqjV6p\ntzDbktfJEBr1BF5/y171f8HGtp3dp9cz5RD3XLlDvb+1UsSd+7fFro8NEjqzYYp1pt+cpZkynjxZ\nVcyTSinA8npTXd9S6GNhqqDmJhKLqzXbRmCIMFUM8KbbL1L/p3tPJT0U++LBRiCZ6ZB0jgBwYKd0\nOOk+2poON1887/zd3Zdtd77vAi9bspk5nMI9E0qnhFh/urzCYjoIM/Dksw4fgCPowAOOgaybPb7W\nkGvyTElrOrSpvEI4163FmbLBoOIMHN8TCANh1McrUeMhMR0oA6nOxTfXB84OsBlkNDbI4KU5+y7r\nGXnl9buc28yTPCiFvnOtmZ0o4AXRWmmXNtrHB1hMh3rTef1C32PdK3RGu9poq/algKQvE5V578Ik\nTkbPIJWB1VqRpkNgrg8q2BJoJ74Q+LFnEwB2Mg0LowVf4GNxpqyYARcuTOKt9+7Hz/zZN9QxlUI5\nHqXNIef2pdkyPv3wMVSbbXS6XYN6TkEBThc3NKDCeHkFsQgoi11nrAohiOlALTOjEgVfwGf9hkNP\nYHaiYMyxadi7MIm9C5P4zMPH1HvTpQCekMmGLRMFPHZ8XQWS+Xh40ZU78JmHj+Hzj5xQ79HcWSnK\nZFelJJkq5LxPFnyVmSe9gEopVM+pq2UmsQGMDhitjtZ0CKWmAxeTpBI5V3kQwbQvYATrFdOh3lbr\nBTmsdAyz5QJOrDcM4dJy6KskEO9scmS5ih1RqcIP3LTXSIzRNeDbpusTRloStL9nT9exXG3GdM54\n61a6dnvmJvDmuy/Bd4+u4fOPnIAQwHSpgpWazOC3GXs6Scy9HPrKF+F2+5Hlmno23vnSyzFd0ozb\ngs86ObXayscpBnreCQPJ1iTWlBSSNBNjvicwVQywWjdtyGn+7IbaflxvuhNgHErTwcF04Pbuaq1l\nsMlbkfgkCUm2IyHJfstCzyac+1yOTcSKNRmeWGsYtXekip2m6QDIhf+ZUzU0Wh3Umh10upq6C5hq\nylnZZzt7K6LyCkDToFyUSl0vF6p9SCfCLazTa8tGAk16a1anj6nUoENZGXxLs2WjfSJ3HNJaPtHx\n9mookgNIQnlHVy2mg5dcL28jyTCzM9O8ZSYXOxzEyOXgehL2NqlfOSlb9wK7HptD04DdLTHJ0F6t\ntZyLgF3WY3ezAGyhp3jQgW838JI0HboqHywEE2SN3qQFjDMlgLimA6drp8HUdDCPl4PT9XknC84Q\novd5y0z6XlL3Cq5ebjsIgGY6aAE4onEKlZGwVaO5pkNacJJ3VvBEelZ7WDCCDhlZB6pLJcdFBVfY\nc7E4o+cmaotXbbaNewSY7W8JfDxOsbpYEXO++78WfP4Y9jVVKuMuZhIxm1iNLo29QkDlFXKMtLu6\nvCKd6WDuwxCnjGj4T0WdhpZmy1oXpEvdWeLrmfxuySyvEJRN10yHBqOq15ptNDv5mWAu8C4PvhDG\nM2Svz4a2gLVOUgCyG7HL8oqO0jb7XceTtleyAurCmlO4kKQruEpq8YC0Cega16MgAq1XrU5Xtavl\nSRwKOlA5RsHXz2HAnOAiE5VMugZLhg3Ggw7x79N3Hzu+LvcbraO1pm6PuDRbwnqjjW4XOLbawLE1\n7fi5yisMpgO3K6PrRvoVsr0sd4KpxWDXcIzk9k1WTb8dWOxOXHRssxMFtCLqO7/edP6LUScVQJZ8\n0utKKUSj1VF2IH+fsDVisnIxRZeQ5Ew0Buos6LBSa6Le7KhjrTbbBsPsu1HQIa17hR1A513puOAg\nt59p34AMhtQaJCSpE1B03Eei8bxjuoQjTJjelYRxvUdjkOYPCr6cqjbVfEM6RE8urxu/rUbXhra9\nUmuqBFAxlExsxfq0bR+LkcOPh+7jU8tVFXSg4+Ritlx42LXeJWo6WDaqrXdnv6aEq+/pIH8604Hm\nn+TyCkDaSXwstjoySOML+Ty2OqRLMw46jDEAdNBBTobL601VKwhERoAwJycXCoFnlFAA7gUvT409\np5QCpqGmKM+OhYZ+R4rRgBkhJGjnLzk6mAYlMBNrL5o8VPm1WJwp60ndNwXe0ko0aPLo1VDkZSfT\npUC3xLIMAPk6fUKxu2sQYpoOqrzCbIk4LBEaU0/CdoQimmA9Tn/NgpGdtY411trNMqzp/ytJ3Sss\nNohmPrgzEdxhcQXKuIgZb5nZ7urWggKa2qk1HRjTgbNmWK0gwJkO6fesYAQd5HuuccQz57RNAXNO\noNvlLq+gwFg8I113BEXpe42W7PtOivina8lMB/k6VJkRIN2pUSyeKAPAT3tUa3MvzxPNO4rlUjRF\nvOR3tFbKTFkbu4LdL74NDj5eedCVHCT1/0GCDp779TBAc6NrLeAZavoeZ+KEvm6ZyUuY+Ln6MU0H\ne06Jz2OPR52GlmbLKnhDonWh52Y6bJ/WrAhuUDejkrZCVNOsyisa7ZjGTq/ga4DvC2OttddnbtS7\ngvMuodws8Iz/MJDIdIg2T0FFHnRwBbVprEwWfCXiCWhVe9JaanKmwzwTNWRUf67pUAgkG6dslVfQ\naxfIrgNM59c1pxHr4bETawh9HTyrNvV6ZidFHnpmRb0+HgUg0jQdNIPWtBPWGi1DSDKIxmy91Y61\ncQ9YS26g/0QGv8/lgj62uYlQKfUHvlDrBiDPn1+D6/fOqdeVyLbigp2A2QKSknvTKvirA1SA1nTg\nTAcKLKzUZAcM0saoNeVntHZ9N7K/Xd1XCHZSwwg6OGwqzt4EgJkJWV7RaOkuK+XoWADJbtgyEeLi\nbZM4slxVASOXXesMOlCL+GjbVCJMazigx+A3n9Zjr9nuoNZos/U8xGq9hdV6C1ORPyBbQbu1qnig\nhgcdufDs06eqOLJcw1QxUOfD/REah7Wm2zGnIBrd41a7i2Yr3rZY+0puUUltb3mx91xIYzpwQU7e\n9haQQc9W1OaeygrbHaR2zDpXMA46jBA06Ejv4MS6ZDpwZWhTSNK9naIj6OCiMMRxu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0kAM3bE2HqUiM0dZ06AX2/JIp8joEIcm07QLxUuO8yHudQ99jndbk65iQZMK4Iy2r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\nOWg88Xs6WQyGaidesn0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            "text/plain": [
              "<Figure size 1296x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fhkUbrMeLZKU",
        "colab_type": "code",
        "outputId": "ac3f7649-f89a-4ca0-b9fb-426658f5ca92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "# Filter\n",
        "filter = signal.firwin(600, [0.01, 0.06], pass_zero=False)\n",
        "plt.plot(filter)\n",
        "plt.show()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GmFONMeskvS7pUwcFEgAAoMBkcgqGlAwgmIIBAEBhynkTSmvta9bauZJOkXSbMab4SPsZ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\nJCAAAAAAAIDnSEAAAAAAAADP/X9/aSN+yDJ5lwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1296x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pztig4WnXegY",
        "colab_type": "code",
        "outputId": "a4cf05b2-0a0f-4c38-8c99-b6dabe43cf03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# Normalized values\n",
        "data_norm_to_0_1 = [number/scipy.linalg.norm(f3_channel_eeg.squeeze()) for number in f3_channel_eeg.squeeze()]\n",
        "# Applied filter to normalized signal\n",
        "filtered_and_normalized = signal.convolve(data_norm_to_0_1, filter, mode=\"same\")\n",
        "plt.plot(filtered_and_normalized)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe3a71296a0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TsDqDkW/3lvGfdbnkVbcwPNiTR88bwrThgd2qkDCZFO9tzOPl1QcYFuTJB3OS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\ngg==\n",
            "text/plain": [
              "<Figure size 1296x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jwIQqRUKGnVo",
        "colab_type": "text"
      },
      "source": [
        "## Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "blL4Ys6KnxtS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "9614bf29-6941-4301-d71b-a77e05d3afd3"
      },
      "source": [
        "df_eeg.head()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>eeg</th>\n",
              "      <th>truth</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>./Finalised/User_0/run_0/EEG.csv</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>./Finalised/User_0/run_1/EEG.csv</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>./Finalised/User_0/run_2/EEG.csv</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>./Finalised/User_0/run_3/EEG.csv</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>./Finalised/User_0/run_4/EEG.csv</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index                               eeg  truth\n",
              "0      0  ./Finalised/User_0/run_0/EEG.csv      1\n",
              "1      1  ./Finalised/User_0/run_1/EEG.csv      0\n",
              "2      2  ./Finalised/User_0/run_2/EEG.csv      0\n",
              "3      3  ./Finalised/User_0/run_3/EEG.csv      0\n",
              "4      4  ./Finalised/User_0/run_4/EEG.csv      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qCjZXl2enaKK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "0bddcfb9-1fee-47fb-b177-4f9245c2ac5b"
      },
      "source": [
        "length_of_trial = []\n",
        "data = []\n",
        "label = []\n",
        "for i, eeg in df_eeg.iterrows():\n",
        "  if i % 20 == 0:\n",
        "    print(\"Processed trials:\", i)\n",
        "  df_trial = pd.read_csv(eeg[1]).iloc[:,1:-1]\n",
        "  # Convert to numeric and drop N/A values\n",
        "  df_trial = df_trial.apply(lambda x: pd.to_numeric(x, errors = 'coerce')).dropna()\n",
        "  # Filter EEG table only for values (except for X and Y Value)\n",
        "  df_trial = df_trial.loc[:, df_trial.columns.str.contains('Value')]\n",
        "  if i >= 1:\n",
        "    length_of_trial.append((length_of_trial[i-1][1]+1, length_of_trial[i-1][1] + df_trial.shape[0]))\n",
        "  else:\n",
        "    length_of_trial.append((0, df_trial.shape[0]))\n",
        "  for col in df_trial:\n",
        "    # Normalize and filter\n",
        "    data_norm_to_0_1 = [number/scipy.linalg.norm(df_trial[col].squeeze()) for number in df_trial[col].squeeze()]\n",
        "    filtered_and_normalized = signal.convolve(data_norm_to_0_1, filter, mode=\"same\")\n",
        "    df_trial[col] = filtered_and_normalized\n",
        "    df_trial[\"label\"] = eeg[2]\n",
        "  for j, row in df_trial.iterrows():\n",
        "    data.append(row.values[:-1])\n",
        "    label.append(row.values[-1])\n",
        "\n",
        "  #for j, el in enumerate(combined_vec):\n",
        "  #  data[i][j] = combined_vec[j]\n",
        "  \n",
        "  #min_max_scaler = preprocessing.MinMaxScaler()\n",
        "  #x_scaled = min_max_scaler.fit_transform(x)\n",
        "  \n",
        "  #label.append(eeg[2])\n",
        "#label = np.asarray(label)\n",
        "print(data.__len__())\n",
        "print(label.__len__())\n",
        "data = np.asarray(data)\n",
        "label = np.asarray(label)"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Processed trials: 0\n",
            "Processed trials: 20\n",
            "Processed trials: 40\n",
            "Processed trials: 60\n",
            "Processed trials: 80\n",
            "Processed trials: 100\n",
            "Processed trials: 120\n",
            "Processed trials: 140\n",
            "Processed trials: 160\n",
            "Processed trials: 180\n",
            "Processed trials: 200\n",
            "271427\n",
            "271427\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIA_4hRPdG-u",
        "colab_type": "code",
        "outputId": "805ebcc5-af08-46e9-fff4-bd7da8ef4b78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(data.shape)\n",
        "print(label.shape)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(271427, 15)\n",
            "(271427,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BPPeAEopml-t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "fe773113-8001-4f03-c66c-8cb32339ddbc"
      },
      "source": [
        "accuracies = []\n",
        "kf = KFold(n_splits=5, shuffle=True)\n",
        "for train_index, test_index in kf.split(length_of_trial, length_of_trial):\n",
        "  X_train, y_train = [], []\n",
        "  for index in train_index:\n",
        "    start, end = length_of_trial[index]\n",
        "    for row in data[start:end]:\n",
        "      X_train.append(row)\n",
        "    for row in label[start:end]:\n",
        "      y_train.append(row)\n",
        "  learned_model = GaussianNB().fit(X_train, y_train)\n",
        "\n",
        "  score = 0\n",
        "  for index in test_index:\n",
        "    X_test, y_test = [], []\n",
        "    start, end = length_of_trial[index]\n",
        "    for row in data[start:end]:\n",
        "      X_test.append(row)\n",
        "    for row in label[start:end]:\n",
        "      y_test.append(row)\n",
        "    prediction = learned_model.predict_proba(np.asarray(X_test))\n",
        "    mysum = 0\n",
        "    for p in prediction:\n",
        "      mysum += p[1]\n",
        "    if mysum/X_test.__len__() >= 0.5:\n",
        "    #if sum(prediction == 1) >= X_test.__len__() / 2:\n",
        "      pred = 1\n",
        "    else:\n",
        "      pred = 0\n",
        "    if y_test[0] == pred:\n",
        "      score += 1\n",
        "  accuracies.append(score/test_index.__len__())\n",
        "  print(score/test_index.__len__())\n",
        "print(\"Model {} with avg test accuracy: {}\".format(GaussianNB(), sum(accuracies)/accuracies.__len__()))  "
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.5365853658536586\n",
            "0.4\n",
            "0.575\n",
            "0.675\n",
            "0.7\n",
            "Model GaussianNB(priors=None, var_smoothing=1e-09) with avg test accuracy: 0.5773170731707318\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}